Label distribution learning.
Geng et al. [1]} pioneered a new machine learning paradigm: LDL, which conveys richer semantics by converting labels into descriptive degrees.
Subsequently, numerous studies [1]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [12]}, [13]}, [14]}, [15]} are opened for LDL tasks, which involve both applications and pure theory.
One of the papers [13]} is very interested in modeling the uncertainty of the label distribution values via deep networks.
Inspired by this, we address the feature space at small scales to model uncertainty to offer richer materials for downstream models.
